- Several bugfixes

- Issue #2 - PEP 8 compliance
  - Issue #3 - Code documentation with PEP 257 compliance

benchmarks/Measures.py

@@ -205,12 +205,12 @@ def get_point_statistics(data, model, indexer=None):
     else:
         ndata = np.array(data[model.order:])
 
-    if model.isMultivariate or indexer is None:
+    if model.is_multivariate or indexer is None:
         forecasts = model.forecast(data)
-    elif not model.isMultivariate and indexer is not None:
+    elif not model.is_multivariate and indexer is not None:
         forecasts = model.forecast(indexer.get_data(data))
 
-    if model.hasSeasonality:
+    if model.has_seasonality:
         nforecasts = np.array(forecasts)
     else:
         nforecasts = np.array(forecasts[:-1])

benchmarks/benchmarks.py

@@ -122,7 +122,7 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
                     _key = mfts.shortname + " " + pttr + " q = " + str(partition)
 
                     mfts.partitioner = data_train_fs
-                    if not mfts.isHighOrder:
+                    if not mfts.is_high_order:
 
                         if dump: print(ct,_key)
 
@@ -155,7 +155,7 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
 
                     else:
                         for order in np.arange(1, max_order + 1):
-                            if order >= mfts.minOrder:
+                            if order >= mfts.min_order:
                                 mfts = model("")
 
                                 _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition)
@@ -225,7 +225,7 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
     for count, model in enumerate(models, start=0):
         #print(model)
         mfts = model("")
-        if not mfts.isHighOrder:
+        if not mfts.is_high_order:
             if transformation is not None:
                 mfts.appendTransformation(transformation)
             mfts.train(data_train, data_train_fs.sets)
@@ -233,7 +233,7 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
             lcolors.append( colors[count % ncol] )
         else:
             for order in np.arange(1,max_order+1):
-                if order >= mfts.minOrder:
+                if order >= mfts.min_order:
                     mfts = model(" n = " + str(order))
                     if transformation is not None:
                         mfts.appendTransformation(transformation)
@@ -387,7 +387,7 @@ def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners
                     _key = mfts.shortname + " " + pttr+ " q = " +str(partition)
 
                     mfts.partitioner = data_train_fs
-                    if not mfts.isHighOrder:
+                    if not mfts.is_high_order:
 
                         if dump: print(ct,_key)
 
@@ -418,7 +418,7 @@ def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners
 
                     else:
                         for order in np.arange(1, max_order + 1):
-                            if order >= mfts.minOrder:
+                            if order >= mfts.min_order:
                                 mfts = model("")
                                 _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition)
                                 mfts.partitioner = data_train_fs
@@ -467,7 +467,7 @@ def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save
 
     for count, model in Util.enumerate2(models, start=0, step=2):
         mfts = model("")
-        if not mfts.isHighOrder:
+        if not mfts.is_high_order:
             if transformation is not None:
                 mfts.appendTransformation(transformation)
             mfts.train(data_train, data_train_fs)
@@ -475,7 +475,7 @@ def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save
             lcolors.append( colors[count % ncol] )
         else:
             for order in np.arange(1,max_order+1):
-                if order >= mfts.minOrder:
+                if order >= mfts.min_order:
                     mfts = model(" n = " + str(order))
                     if transformation is not None:
                         mfts.appendTransformation(transformation)
@@ -522,7 +522,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
     ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
 
     for count, fts in enumerate(models, start=0):
-        if fts.hasPointForecasting and points:
+        if fts.has_point_forecasting and points:
             forecasted = fts.forecast(original)
             mi.append(min(forecasted) * 0.95)
             ma.append(max(forecasted) * 1.05)
@@ -532,7 +532,7 @@ def plot_compared_series(original, models, colors, typeonlegend=False, save=Fals
             if typeonlegend: lbl += " (Point)"
             ax.plot(forecasted, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
 
-        if fts.hasIntervalForecasting and intervals:
+        if fts.has_interval_forecasting and intervals:
             forecasted = fts.forecastInterval(original)
             lower = [kk[0] for kk in forecasted]
             upper = [kk[1] for kk in forecasted]
@@ -660,7 +660,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
                     _key = mfts.shortname + " " + pttr+ " q = " +str(partition)
 
                     mfts.partitioner = data_train_fs
-                    if not mfts.isHighOrder:
+                    if not mfts.is_high_order:
 
                         if dump: print(ct,_key)
 
@@ -692,7 +692,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
 
                     else:
                         for order in np.arange(1, max_order + 1):
-                            if order >= mfts.minOrder:
+                            if order >= mfts.min_order:
                                 mfts = model("")
                                 _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition)
                                 mfts.partitioner = data_train_fs
@@ -743,7 +743,7 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
 
     for count, model in Util.enumerate2(models, start=0, step=2):
         mfts = model("")
-        if not mfts.isHighOrder:
+        if not mfts.is_high_order:
             if transformation is not None:
                 mfts.appendTransformation(transformation)
             mfts.train(data_train, data_train_fs)
@@ -751,7 +751,7 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
             lcolors.append( colors[count % ncol] )
         else:
             for order in np.arange(1,max_order+1):
-                if order >= mfts.minOrder:
+                if order >= mfts.min_order:
                     mfts = model(" n = " + str(order))
                     if transformation is not None:
                         mfts.appendTransformation(transformation)
@@ -822,7 +822,7 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
     ma = []
 
     for count, fts in enumerate(models, start=0):
-        if fts.hasDistributionForecasting and distributions[count]:
+        if fts.has_probability_forecasting and distributions[count]:
             density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to,
                                                     resolution=resolution, method=option)
 
@@ -863,7 +863,7 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
             cb.set_label('Density')
 
 
-        if fts.hasIntervalForecasting:
+        if fts.has_interval_forecasting:
             forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to)
             lower = [kk[0] for kk in forecasts]
             upper = [kk[1] for kk in forecasts]
@@ -939,7 +939,7 @@ def SelecaoSimples_MenorRMSE(original, parameters, modelo):
     min_rmse = 100000.0
     best = None
     for p in parameters:
-        sets = Grid.GridPartitionerTrimf(original, p)
+        sets = Grid.GridPartitioner(original, p).sets
         fts = modelo(str(p) + " particoes")
         fts.train(original, sets)
         # print(original)
@@ -1095,7 +1095,7 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
             fts.train(train, sets, o, parameters=parameters)
             if not intervals:
                 forecasted = fts.forecast(test)
-                if not fts.hasSeasonality:
+                if not fts.has_seasonality:
                     error = Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1]))
                 else:
                     error = Measures.rmse(np.array(test[o:]), np.array(forecasted))
@@ -1171,7 +1171,7 @@ def sliding_window_simple_search(data, windowsize, model, partitions, orders, sa
                 fts.train(data, sets, o, parameters=parameters)
                 if not intervals:
                     forecasted = fts.forecast(test)
-                    if not fts.hasSeasonality:
+                    if not fts.has_seasonality:
                         _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1])) )
                     else:
                         _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted)) )
@@ -1221,7 +1221,7 @@ def sliding_window_simple_search(data, windowsize, model, partitions, orders, sa
 
 def pftsExploreOrderAndPartitions(data,save=False, file=None):
     fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[6, 8])
-    data_fs1 = Grid.GridPartitionerTrimf(data, 10)
+    data_fs1 = Grid.GridPartitioner(data, 10).sets
     mi = []
     ma = []
 
@@ -1250,7 +1250,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
     axes[3].set_title('Interval Forecasts by Number of Partitions')
 
     for partitions in np.arange(5, 11):
-        data_fs = Grid.GridPartitionerTrimf(data, partitions)
+        data_fs = Grid.GridPartitioner(data, partitions).sets
         fts = pwfts.ProbabilisticWeightedFTS("")
         fts.shortname = "q = " + str(partitions)
         fts.train(data, data_fs, 1)

benchmarks/distributed_benchmarks.py

@@ -74,9 +74,9 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
     for model in models:
         mfts = model("")
 
-        if mfts.isHighOrder:
+        if mfts.is_high_order:
             for order in np.arange(1, max_order + 1):
-                if order >= mfts.minOrder:
+                if order >= mfts.min_order:
                     mfts = model("")
                     mfts.order = order
                     pool.append(mfts)
@@ -193,9 +193,9 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
     for model in models:
         mfts = model("")
 
-        if mfts.isHighOrder:
+        if mfts.is_high_order:
             for order in np.arange(1, max_order + 1):
-                if order >= mfts.minOrder:
+                if order >= mfts.min_order:
                     mfts = model("")
                     mfts.order = order
                     pool.append(mfts)

chen.py

@@ -25,7 +25,7 @@ class ConventionalFLRG(object):
 
 
 class ConventionalFTS(fts.FTS):
-    def __init__(self, order, **kwargs):
+    def __init__(self, name, **kwargs):
         super(ConventionalFTS, self).__init__(1, "CFTS " + name)
         self.name = "Conventional FTS"
         self.detail = "Chen"

cheng.py

@@ -31,7 +31,7 @@ class TrendWeightedFLRG(yu.WeightedFTS):
 
 
 class TrendWeightedFTS(yu.WeightedFTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(TrendWeightedFTS, self).__init__(1, "TWFTS " + name)
         self.name = "Trend Weighted FTS"
         self.detail = "Cheng"

ensemble.py

@@ -10,7 +10,7 @@ from pyFTS import fts
 
 
 class EnsembleFTS(fts.FTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(EnsembleFTS, self).__init__("Ensemble FTS")
         self.shortname = "Ensemble FTS " + name
         self.name = "Ensemble FTS"

hofts.py

@@ -39,7 +39,7 @@ class HighOrderFLRG(object):
 
 
 class HighOrderFTS(fts.FTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
         self.name = "High Order FTS"
         self.shortname = "HOFTS" + name

hwang.py

@@ -4,7 +4,7 @@ from pyFTS import fts
 
 
 class HighOrderFTS(fts.FTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(HighOrderFTS, self).__init__(1, name)
         self.is_high_order = True
         self.min_order = 2

ifts.py

@@ -7,7 +7,7 @@ from pyFTS import hofts, fts, tree
 
 
 class IntervalFTS(hofts.HighOrderFTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(IntervalFTS, self).__init__(order=1, name="IFTS " + name)
         self.shortname = "IFTS " + name
         self.name = "Interval FTS"

ismailefendi.py

@@ -33,7 +33,7 @@ class ImprovedWeightedFLRG(object):
 
 
 class ImprovedWeightedFTS(fts.FTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name)
         self.name = "Improved Weighted FTS"
         self.detail = "Ismail & Efendi"

sadaei.py

@@ -37,7 +37,7 @@ class ExponentialyWeightedFLRG(object):
 
 
 class ExponentialyWeightedFTS(fts.FTS):
-    def __init__(self, order, **kwargs):
+    def __init__(self, name, **kwargs):
         super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS")
         self.name = "Exponentialy Weighted FTS"
         self.detail = "Sadaei"

sfts.py

@@ -26,7 +26,7 @@ class SeasonalFLRG(FLR.FLR):
 
 
 class SeasonalFTS(fts.FTS):
-    def __init__(self, order, **kwargs):
+    def __init__(self, name, **kwargs):
         super(SeasonalFTS, self).__init__(1, "SFTS")
         self.name = "Seasonal FTS"
         self.detail = "Chen"

tests/general.py

@@ -12,14 +12,14 @@ import pandas as pd
 from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
 from pyFTS.common import FLR,FuzzySet,Membership,Transformations
 from pyFTS import fts,hofts,ifts,pwfts,tree, chen
-from pyFTS.benchmarks import benchmarks as bchmk
+#from pyFTS.benchmarks import benchmarks as bchmk
 from pyFTS.benchmarks import naive, arima
 from pyFTS.benchmarks import Measures
 from numpy import random
 
 #print(FCM.FCMPartitionerTrimf.__module__)
 
-#gauss = random.normal(0,1.0,5000)
+#gauss = random.normal(0,1.0,1000)
 #gauss_teste = random.normal(0,1.0,400)
 
 
@@ -31,14 +31,14 @@ taiex = np.array(taiexpd["avg"][:5000])
 #from statsmodels.tsa.arima_model import ARIMA as stats_arima
 from statsmodels.tsa.tsatools import lagmat
 
-tmp = np.arange(10)
+#tmp = np.arange(10)
 
-lag, a = lagmat(tmp, maxlag=2, trim="both", original='sep')
+#lag, a = lagmat(tmp, maxlag=2, trim="both", original='sep')
 
-print(lag)
+#print(lag)
-print(a)
+#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
@@ -52,11 +52,11 @@ print(a)
 
 #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=False, save=True, file="experiments/nasdaq_point_distributed.csv",
+                     dump=False, save=False, file="experiments/nasdaq_point_distributed.csv",
-#                     nodes=['192.168.0.109', '192.168.0.101']) #, depends=[hofts, ifts])
+                     nodes=['192.168.1.42']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
 
@@ -83,10 +83,10 @@ print(a)
 #                                    gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
 
 
-#bchmk.interval_sliding_window(nasdaq,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
+bchmk.interval_sliding_window(gauss,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
-#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-#                     partitions= np.arange(10,200,step=5), #
+                     partitions= np.arange(10,200,step=5), #
-#                     dump=True, save=True, file="experiments/nasdaq_interval.csv")
+                     dump=True, save=False, file="experiments/nasdaq_interval.csv")
 
 #3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
 #                    partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],

tests/pwfts.py

@@ -53,8 +53,8 @@ pfts3_enrollments.train(enrollments, enrollments_fs1, 3)
 
 bchmk.plot_compared_series(enrollments,[pfts1_enrollments,pfts2_enrollments, pfts3_enrollments],
                            ["red","blue","green"], linewidth=2,
-                         typeonlegend=True,save=True,file="pictures/pwfts_enrollments_interval.png",
+                         typeonlegend=True,save=False,file="pictures/pwfts_enrollments_interval.png",
-                           tam=[20,7],points=False, intervals=False)
+                           tam=[20,7],points=False, intervals=True)
 
 
 

yu.py

@@ -31,7 +31,7 @@ class WeightedFLRG(fts.FTS):
 
 
 class WeightedFTS(fts.FTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
         super(WeightedFTS, self).__init__(1, "WFTS " + name)
         self.name = "Weighted FTS"
         self.detail = "Yu"