- Deep refactor on project folders

pyFTS/benchmarks/arima.py

@@ -5,8 +5,7 @@ import numpy as np
 import pandas as pd
 from statsmodels.tsa.arima_model import ARIMA as stats_arima
 import scipy.stats as st
-from pyFTS import fts
+from pyFTS.common import SortedCollection, fts
-from pyFTS.common import SortedCollection
 from pyFTS.probabilistic import ProbabilityDistribution
 
 

pyFTS/benchmarks/benchmarks.py

@@ -13,14 +13,14 @@ import matplotlib.cm as cmx
 import matplotlib.colors as pltcolors
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 #from mpl_toolkits.mplot3d import Axes3D
 
 from pyFTS.probabilistic import ProbabilityDistribution
-from pyFTS import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, ensemble, hwang
+from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
+from pyFTS.models.ensemble import ensemble
 from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, quantreg
 from pyFTS.benchmarks import Util as bUtil
-from pyFTS.common import Transformations, Util
+from pyFTS.common import Util
 # from sklearn.cross_validation import KFold
 from pyFTS.partitioners import Grid
 from matplotlib import rc
@@ -817,7 +817,6 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
 def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to):
     from matplotlib.patches import Rectangle
     from matplotlib.collections import PatchCollection
-    from  matplotlib.colorbar import ColorbarPatch
     patches = []
     colors = []
     for x in density.index:
@@ -840,7 +839,6 @@ from pyFTS.common import Transformations
 def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_from):
     from matplotlib.patches import Rectangle
     from matplotlib.collections import PatchCollection
-    from  matplotlib.colorbar import ColorbarPatch
     patches = []
     colors = []
     for ct, dt in enumerate(probabilitydist):

pyFTS/benchmarks/distributed_benchmarks.py

@@ -8,13 +8,12 @@ python3 /usr/local/bin/dispynode.py -i [local IP] -d
 
 import datetime
 import time
-from copy import deepcopy
 
 import dispy
 import dispy.httpd
 import numpy as np
 
-from pyFTS.benchmarks import benchmarks, Util as bUtil, naive, quantreg, arima
+from pyFTS.benchmarks import benchmarks, Util as bUtil, quantreg, arima
 from pyFTS.common import Util
 from pyFTS.partitioners import Grid
 
@@ -32,7 +31,8 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
     :return: a dictionary with the benchmark results 
     """
     import time
-    from pyFTS import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei, song, cheng, hwang
+    from pyFTS import yu, hofts, pwfts,ismailefendi,sadaei, song, cheng, hwang
+    from pyFTS.models import chen
     from pyFTS.partitioners import Grid, Entropy, FCM
     from pyFTS.benchmarks import Measures, naive, arima, quantreg
     from pyFTS.common import Transformations
@@ -424,9 +424,10 @@ def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, windo
     """
     import time
     import numpy as np
-    from pyFTS import hofts, ifts, pwfts, ensemble
+    from pyFTS import hofts, ifts, pwfts
+    from pyFTS.models import ensemble
     from pyFTS.partitioners import Grid, Entropy, FCM
-    from pyFTS.benchmarks import Measures, arima, quantreg
+    from pyFTS.benchmarks import Measures, arima
     from pyFTS.models.seasonal import SeasonalIndexer
 
     tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS, arima.ARIMA, ensemble.AllMethodEnsembleFTS]

pyFTS/benchmarks/naive.py

@@ -1,7 +1,7 @@
 #!/usr/bin/python
 # -*- coding: utf8 -*-
 
-from pyFTS import fts
+from pyFTS.common import fts
 
 
 class Naive(fts.FTS):

pyFTS/benchmarks/quantreg.py

@@ -5,8 +5,7 @@ import numpy as np
 import pandas as pd
 from statsmodels.regression.quantile_regression import QuantReg
 from statsmodels.tsa.tsatools import lagmat
-from pyFTS import fts
+from pyFTS.common import SortedCollection, fts
-from pyFTS.common import SortedCollection
 from pyFTS.probabilistic import ProbabilityDistribution
 
 class QuantileRegression(fts.FTS):

pyFTS/models/chen.py

@@ -5,8 +5,7 @@ S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet, FLR
+from pyFTS.common import FuzzySet, FLR, fts, flrg
-from pyFTS import fts,flrg
 
 
 class ConventionalFLRG(flrg.FLRG):

pyFTS/models/cheng.py

@@ -6,8 +6,8 @@ Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts
-from pyFTS import fts, yu
+from pyFTS.models import yu
 
 
 class TrendWeightedFLRG(yu.WeightedFLRG):

pyFTS/models/ensemble/ensemble.py

@@ -3,16 +3,10 @@
 
 import numpy as np
 import pandas as pd
-import math
+from pyFTS.common import SortedCollection, fts, tree
-from operator import itemgetter
+from pyFTS.models import chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
-from pyFTS.common import FLR, FuzzySet, SortedCollection
-from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
-from pyFTS.benchmarks import arima, quantreg
-from pyFTS.common import Transformations
 import scipy.stats as st
-from pyFTS import tree
+
-from pyFTS.seasonal import sfts, msfts
-from pyFTS.probabilistic import ProbabilityDistribution, kde
 
 def sampler(data, quantiles):
     ret = []

pyFTS/models/ensemble/multiseasonal.py

@@ -2,17 +2,10 @@
 # -*- coding: utf8 -*-
 
 import numpy as np
-import pandas as pd
+from pyFTS.common import Util as cUtil
-import math
+from pyFTS.models.ensemble import ensemble
-from operator import itemgetter
+from pyFTS.models.seasonal import cmsfts
-from pyFTS.common import FLR, FuzzySet, SortedCollection
+from pyFTS.probabilistic import ProbabilityDistribution
-from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu, sfts
-from pyFTS.benchmarks import arima, quantreg
-from pyFTS.common import Transformations, Util as cUtil
-import scipy.stats as st
-from pyFTS.ensemble import ensemble
-from pyFTS.models import msfts, cmsfts
-from pyFTS.probabilistic import ProbabilityDistribution, kde
 from copy import deepcopy
 from joblib import Parallel, delayed
 import multiprocessing

pyFTS/models/hofts.py

@@ -6,9 +6,7 @@ Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts, flrg, tree
-from pyFTS import fts, flrg, tree
-
 
 class HighOrderFLRG(flrg.FLRG):
     """Conventional High Order Fuzzy Logical Relationship Group"""

pyFTS/models/hwang.py

@@ -6,8 +6,7 @@ Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR,Transformations
+from pyFTS.common import FuzzySet, FLR, Transformations, fts
-from pyFTS import fts
 
 
 class HighOrderFTS(fts.FTS):

pyFTS/models/ifts.py

@@ -2,8 +2,8 @@
 # -*- coding: utf8 -*-
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts, tree
-from pyFTS import hofts, fts, tree
+from pyFTS.models import hofts
 
 
 class IntervalFTS(hofts.HighOrderFTS):

pyFTS/models/ismailefendi.py

@@ -6,8 +6,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts, flrg
-from pyFTS import fts, flrg
 
 
 class ImprovedWeightedFLRG(flrg.FLRG):

pyFTS/models/nonstationary/common.py

@@ -9,7 +9,7 @@ import numpy as np
 from pyFTS import *
 from pyFTS.common import FuzzySet as FS, Membership, FLR
 from pyFTS.partitioners import partitioner
-from pyFTS.nonstationary import perturbation
+from pyFTS.models.nonstationary import perturbation
 
 
 class FuzzySet(FS.FuzzySet):

pyFTS/models/nonstationary/cvfts.py

@@ -1,7 +1,7 @@
 import numpy as np
-from pyFTS import fts, flrg, chen
+from pyFTS.models import chen
-from pyFTS.nonstationary import common, perturbation, nsfts
+from pyFTS.models.nonstationary import common,nsfts
-from pyFTS.common import Transformations, FuzzySet, FLR
+from pyFTS.common import FLR
 
 
 class ConditionalVarianceFTS(chen.ConventionalFTS):

pyFTS/models/nonstationary/flrg.py

@@ -1,6 +1,6 @@
 
-from pyFTS import flrg
+from pyFTS.common import flrg
-from pyFTS.nonstationary import common
+from pyFTS.models.nonstationary import common
 
 
 class NonStationaryFLRG(flrg.FLRG):

pyFTS/models/nonstationary/honsfts.py

@@ -1,8 +1,7 @@
 import numpy as np
-from pyFTS.common import FuzzySet, FLR
+from pyFTS.common import FuzzySet, FLR, fts, tree
-from pyFTS import fts, hofts
+from pyFTS.models import hofts
 from pyFTS.nonstationary import common, flrg
-from pyFTS import tree
 
 
 class HighOrderNonStationaryFLRG(flrg.NonStationaryFLRG):

pyFTS/models/nonstationary/nsfts.py

@@ -1,6 +1,5 @@
 import numpy as np
-from pyFTS.common import FuzzySet, FLR
+from pyFTS.common import FLR, fts
-from pyFTS import fts, chen
 from pyFTS.nonstationary import common, flrg
 
 

pyFTS/models/nonstationary/partitioners.py

@@ -1,6 +1,6 @@
 import numpy as np
 from pyFTS.partitioners import partitioner
-from pyFTS.nonstationary import common, perturbation
+from pyFTS.models.nonstationary import common, perturbation
 
 
 class PolynomialNonStationaryPartitioner(partitioner.Partitioner):

pyFTS/models/pwfts.py

@@ -5,8 +5,8 @@ import numpy as np
 import pandas as pd
 import math
 from operator import itemgetter
-from pyFTS.common import FLR, FuzzySet, SortedCollection
+from pyFTS.common import FLR, FuzzySet, tree
-from pyFTS import hofts, ifts, tree
+from pyFTS.models import hofts, ifts
 from pyFTS.probabilistic import ProbabilityDistribution
 
 

pyFTS/models/sadaei.py

@@ -6,8 +6,7 @@ refined exponentially weighted fuzzy time series and an improved harmony search,
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet,FLR,fts, flrg
-from pyFTS import fts, flrg
 
 
 class ExponentialyWeightedFLRG(flrg.FLRG):

pyFTS/models/seasonal/SeasonalIndexer.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pandas as pd
-from enum import Enum
+from pyFTS.models.seasonal import common
-from pyFTS.seasonal import common
 
 
 class SeasonalIndexer(object):

pyFTS/models/seasonal/cmsfts.py

@@ -1,6 +1,7 @@
 import numpy as np
 from pyFTS.common import FuzzySet, FLR
-from pyFTS import fts, sfts, chen
+from pyFTS.models.seasonal import sfts
+from pyFTS.models import chen
 
 
 class ContextualSeasonalFLRG(object):

pyFTS/models/seasonal/msfts.py

@@ -1,6 +1,6 @@
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FLR
-from pyFTS.seasonal import sfts
+from pyFTS.models.seasonal import sfts
 
 
 class MultiSeasonalFTS(sfts.SeasonalFTS):

pyFTS/models/seasonal/sfts.py

@@ -7,8 +7,7 @@ S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts
-from pyFTS import fts
 
 
 class SeasonalFLRG(FLR.FLR):

pyFTS/models/song.py

@@ -5,8 +5,7 @@ Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Sy
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet, FLR
+from pyFTS.common import FuzzySet, FLR, fts
-from pyFTS import fts
 
 
 class ConventionalFTS(fts.FTS):

pyFTS/models/yu.py

@@ -6,8 +6,8 @@ Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
 """
 
 import numpy as np
-from pyFTS.common import FuzzySet,FLR
+from pyFTS.common import FuzzySet, FLR, fts, flrg
-from pyFTS import fts, flrg, chen
+from pyFTS.models import chen
 
 
 class WeightedFLRG(flrg.FLRG):

pyFTS/tests/cmsfts.py

@@ -12,8 +12,8 @@ import datetime
 
 import pandas as pd
 from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
-from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
+from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
-from pyFTS import fts,sfts
+from pyFTS import sfts
 from pyFTS.models import msfts
 from pyFTS.benchmarks import benchmarks as bchmk
 from pyFTS.benchmarks import Measures

pyFTS/tests/distributed.py

@@ -1,13 +1,14 @@
 from pyFTS.partitioners import Grid
-from pyFTS import fts, flrg, song, chen, yu, sadaei, ismailefendi, cheng, hofts
+from pyFTS.models import chen
 from pyFTS.benchmarks import Measures
-from pyFTS.common import Util as cUtil
+from pyFTS.common import Util as cUtil, fts
 import pandas as pd
 import numpy as np
 import os
 from pyFTS.common import Transformations
 from copy import deepcopy
-from pyFTS.nonstationary import common, flrg, util, perturbation, nsfts, honsfts, partitioners
+from pyFTS.nonstationary import flrg, util, honsfts, partitioners
+from pyFTS.models.nonstationary import nsfts
 
 bc = Transformations.BoxCox(0)
 
@@ -19,10 +20,8 @@ os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
 
 def evaluate_individual_model(model, partitioner, train, test, window_size, time_displacement):
     import numpy as np
-    from pyFTS.common import FLR, FuzzySet
     from pyFTS.partitioners import Grid
     from pyFTS.benchmarks import Measures
-    from pyFTS.nonstationary import common, flrg, util, perturbation, nsfts, honsfts, partitioners
 
     try:
         model.train(train, sets=partitioner.sets, order=model.order, parameters=window_size)

pyFTS/tests/ensemble.py

@@ -3,20 +3,13 @@
 
 import os
 import numpy as np
-import pandas as pd
-import matplotlib as plt
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
-from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
+from pyFTS.partitioners import Grid
-from pyFTS.common import FLR,FuzzySet,Membership,Transformations
+from pyFTS.common import Transformations
-from pyFTS import fts,hofts,ifts,pwfts,tree, chen, song, yu, cheng, ismailefendi, sadaei, hwang
+from pyFTS import hofts, song, yu, cheng, ismailefendi, sadaei, hwang
-from pyFTS.benchmarks import naive, arima
+from pyFTS.models import chen
-from numpy import random
+from pyFTS.models.ensemble import ensemble
-from pyFTS.benchmarks import benchmarks as bchmk
-from pyFTS.benchmarks import arima, quantreg, Measures
-from pyFTS.ensemble import ensemble
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 

pyFTS/tests/general.py

@@ -3,20 +3,13 @@
 
 import os
 import numpy as np
-import pandas as pd
-import matplotlib as plt
 #import matplotlib.pyplot as plt
 #from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
-from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
+from pyFTS.common import Transformations
-from pyFTS.common import FLR,FuzzySet,Membership,Transformations, Util as cUtil
+
-from pyFTS import fts,hofts,ifts,pwfts,tree, chen
 #from pyFTS.benchmarks import benchmarks as bchmk
-from pyFTS.benchmarks import naive, arima
-from pyFTS.benchmarks import Measures
-from numpy import random
-from pyFTS.seasonal import SeasonalIndexer
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 

pyFTS/tests/nonstationary.py

@@ -1,7 +1,8 @@
 import os
 import numpy as np
 from pyFTS.common import Membership, Transformations
-from pyFTS.nonstationary import common,perturbation, partitioners, util,nsfts, honsfts, cvfts
+from pyFTS.nonstationary import common,perturbation, partitioners, util, honsfts, cvfts
+from pyFTS.models.nonstationary import nsfts
 from pyFTS.partitioners import Grid
 import matplotlib.pyplot as plt
 from pyFTS.common import Util as cUtil

pyFTS/tests/pwfts.py

@@ -9,11 +9,9 @@ import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
-from pyFTS.partitioners import Grid
+from pyFTS.common import FLR, FuzzySet, Membership, Transformations, fts
-from pyFTS.common import FLR,FuzzySet,Membership,Transformations
+from pyFTS.models import chen
-from pyFTS import fts,hofts,ifts,pwfts,tree, chen
 from pyFTS.benchmarks import benchmarks as bchmk
-from pyFTS.benchmarks import Measures
 from numpy import random
 
 #gauss_treino = random.normal(0,1.0,1600)

pyFTS/tests/sfts.py

@@ -3,20 +3,10 @@
 
 import os
 import numpy as np
-import pandas as pd
-import matplotlib as plt
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-import datetime
 
 import pandas as pd
-from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
+from pyFTS.common import Util
-from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
-from pyFTS import fts
-from pyFTS.seasonal import sfts, msfts
 from pyFTS.benchmarks import benchmarks as bchmk
-from pyFTS.benchmarks import Measures
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
@@ -38,10 +28,6 @@ sonda_teste = sonda[1051201:]
 #                              tam=[15,8], plotforecasts=False,elev=45, azim=40,
 #                               save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
 
-from pyFTS.models.seasonal import SeasonalIndexer
-from pyFTS.models import msfts
-from pyFTS.common import FLR
-
 partitions = ['grid','entropy']
 
 indexers = ['m15','Mh','Mhm15']

setup.py

@@ -2,11 +2,12 @@ from distutils.core import setup
 
 setup(
     name='pyFTS',
-    packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.ensemble',
+    packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble',
-              'pyFTS.models', 'pyFTS.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
+              'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
-              'pyFTS.tests', 'pyFTS.nonstationary'],
+              'pyFTS.tests', 'pyFTS.models.nonstationary'],
-    package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'], 'ensemble': ['*'], 'models': ['*'],
+    package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'],
-                  'seasonal': ['*'], 'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']},
+                  'models': ['*'], 'seasonal': ['*'], 'ensemble': ['*'],
+                  'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']},
     version='1.1.1',
     description='Fuzzy Time Series for Python',
     author='Petronio Candido L. e Silva',