- Issue #3 - Code documentation with PEP 257 compliance

benchmarks/benchmarks.py

@@ -17,7 +17,7 @@ from mpl_toolkits.mplot3d import Axes3D
 from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
 from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg
 from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, song, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts, cheng, ensemble
+from pyFTS import fts, song, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts, cheng, ensemble, hwang
 from copy import deepcopy
 
 colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
@@ -38,7 +38,7 @@ def get_benchmark_point_methods():
 def get_point_methods():
     """Return all FTS methods for point forecasting"""
     return [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
-            cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS,
+            cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, hwang.HighOrderFTS,
             pwfts.ProbabilisticWeightedFTS]
 
 

chen.py

@@ -1,3 +1,9 @@
+"""
+First Order Conventional Fuzzy Time Series by Chen (1996)
+
+S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet, FLR
 from pyFTS import fts

cheng.py

@@ -1,12 +1,21 @@
+"""
+Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)
+
+C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” 
+Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
 from pyFTS import fts, yu
 
 
-class TrendWeightedFLRG(yu.WeightedFTS):
+class TrendWeightedFLRG(yu.WeightedFLRG):
-    """First Order Trend Weighted Fuzzy Logical Relationship Group"""
+    """
+    First Order Trend Weighted Fuzzy Logical Relationship Group
+    """
     def __init__(self, LHS, **kwargs):
-        super(TrendWeightedFTS, self).__init__(LHS)
+        super(TrendWeightedFLRG, self).__init__(LHS)
 
     def weights(self):
         count_nochange = 0.0
@@ -16,10 +25,10 @@ class TrendWeightedFLRG(yu.WeightedFTS):
 
         for c in self.RHS:
             tmp = 0
-            if self.RHS.midpoint == c.midpoint:
+            if self.LHS.centroid == c.centroid:
                 count_nochange += 1.0
                 tmp = count_nochange
-            elif self.RHS.midpoint > c.midpoint:
+            elif self.LHS.centroid > c.centroid:
                 count_down += 1.0
                 tmp = count_down
             else:
@@ -34,7 +43,7 @@ class TrendWeightedFLRG(yu.WeightedFTS):
 class TrendWeightedFTS(yu.WeightedFTS):
     """First Order Trend Weighted Fuzzy Time Series"""
     def __init__(self, name, **kwargs):
-        super(TrendWeightedFTS, self).__init__(1, "TWFTS " + name)
+        super(TrendWeightedFTS, self).__init__("TWFTS " + name)
         self.name = "Trend Weighted FTS"
         self.detail = "Cheng"
 
@@ -44,6 +53,6 @@ class TrendWeightedFTS(yu.WeightedFTS):
             if flr.LHS.name in flrgs:
                 flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS);
+                flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)

hofts.py

@@ -1,3 +1,10 @@
+"""
+Simple High Order extension of Conventional FTS by Chen (1996)
+
+[1] S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” 
+Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
 from pyFTS import fts

hwang.py

@@ -1,3 +1,10 @@
+"""
+High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)
+
+Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” 
+Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR,Transformations
 from pyFTS import fts

ismailefendi.py

@@ -1,3 +1,10 @@
+"""
+First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)
+
+R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of 
+US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
 from pyFTS import fts

sadaei.py

@@ -1,3 +1,10 @@
+"""
+First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)
+
+H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a 
+refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
 from pyFTS import fts

sfts.py

@@ -1,3 +1,11 @@
+"""
+Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)
+
+Q. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.
+
+S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
 from pyFTS import fts

song.py

@@ -1,9 +1,16 @@
+"""
+First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)
+
+Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
+"""
+
 import numpy as np
 from pyFTS.common import FuzzySet, FLR
 from pyFTS import fts
 
+
 class ConventionalFTS(fts.FTS):
-    """Conventional Fuzzy Time Series"""
+    """Traditional Fuzzy Time Series"""
     def __init__(self, name, **kwargs):
         super(ConventionalFTS, self).__init__(1, "FTS " + name)
         self.name = "Traditional FTS"

tests/general.py

@@ -19,18 +19,18 @@ from numpy import random
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
-enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
+#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
-enrollments = np.array(enrollments["Enrollments"])
+#enrollments = np.array(enrollments["Enrollments"])
 
-from pyFTS import song
+#from pyFTS import song
 
-enrollments_fs = Grid.GridPartitioner(enrollments, 10).sets
+#enrollments_fs = Grid.GridPartitioner(enrollments, 10).sets
 
-model = song.ConventionalFTS('')
+#model = song.ConventionalFTS('')
-model.train(enrollments,enrollments_fs)
+#model.train(enrollments,enrollments_fs)
-teste = model.forecast(enrollments)
+#teste = model.forecast(enrollments)
 
-print(teste)
+#print(teste)
 
 
 #print(FCM.FCMPartitionerTrimf.__module__)
@@ -39,8 +39,8 @@ print(teste)
 #gauss_teste = random.normal(0,1.0,400)
 
 
-#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
-#taiex = np.array(taiexpd["avg"][:5000])
+taiex = np.array(taiexpd["avg"][:5000])
 
 #from statsmodels.tsa.arima_model import ARIMA as stats_arima
 from statsmodels.tsa.tsatools import lagmat
@@ -52,7 +52,7 @@ from statsmodels.tsa.tsatools import lagmat
 #print(lag)
 #print(a)
 
-#from pyFTS.benchmarks import distributed_benchmarks as bchmk
+from pyFTS.benchmarks import distributed_benchmarks as bchmk
 #from pyFTS.benchmarks import parallel_benchmarks as bchmk
 #from pyFTS.benchmarks import benchmarks as bchmk
 #from pyFTS.benchmarks import arima
@@ -66,11 +66,11 @@ from statsmodels.tsa.tsatools import lagmat
 
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
 
-#bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
+bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
-#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-#                     partitions= np.arange(10,200,step=5), #transformation=diff,
+                     partitions= np.arange(10,200,step=5), #transformation=diff,
-#                     dump=True, save=False, file="experiments/nasdaq_point_distributed.csv",
+                     dump=True, save=True, file="experiments/taiex_point_distributed.csv",
-#                     nodes=['192.168.1.42']) #, depends=[hofts, ifts])
+                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
 

yu.py

@@ -1,3 +1,10 @@
+"""
+First Order Weighted Fuzzy Time Series by Yu(2005)
+
+H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” 
+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 import fts